Results of the experiments performed on the employed model are shown in Table REF . We can see that FTA performs better than CT, but ML(\(\theta _0\) ) performs better than FTA. We also see that ML(\(\theta _N^{b^{\prime }}\) ) outperforms all three methods on both test datasets. From this, we can infer that ML(\(\theta _0\) ) can generalize well to any audio and improves further with adaptation on little training data as in ML(\(\theta _N^{b^{\prime }}\) ). Table REF  and Table REF  show the comparison of our meta-learning-based adaptation method ML(\(\theta _N^{b^{\prime }}\) ) to baseline methods on both the test datasets and it is observed that our method outperforms the baseline methods. It is to be considered that none of the baseline methods are adapted to the support set of the test datasets. From this work, we infer that meta-learning-based adaptation when applied to a simple deep CNN model, can improve the results significantly. In general, meta-learning-based adaptation is model-agnostic and can be applied to complex models such as [1]} to improve the results further. A more vivid representation of different experiments can be seen in fig. REF . We can visually compare the performance of different methods in the estimation of pitch contour. Although the estimation with FTA has improved as compared to that with CT, the performance has degraded on those points which were correctly predicted by CT (between 1 and 2 seconds). However, ML(\(\theta _N^{b^{\prime }}\) ) has not only improved the overall prediction as compared to CT, but it is also able to retain the correct predictions with CT.
